About
 

Sendhil Mullainathan

Peter de Florez Professor, MIT Department of Economics and Department of Electrical Engineering and Computer Science

Research Publications

Categories

AI for Good AI in Finance AI in Healthcare Causal Inference Computational Social Science Future of Work Machine Learning Societal Systems

Sendhil Mullainathan is the Peter de Florez Professor with dual appointments in the Department of Economics and the Department of Electrical Engineering and Computer Science (EECS) at MIT, and a principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS). His current research uses machine learning to understand complex problems in human behavior, social policy, and especially medicine, where computational techniques have the potential to uncover biomedical insights from large-scale health data. Mullainathan’s past work combined insights from economics and behavioral science with causal inference tools—lab, field, and natural experiments—to study social problems, such as discrimination and poverty.

He has co-founded the Abdul Latif Jameel Poverty Action Lab at MIT, and ideas42, non-profit to apply behavioral science. In addition, Mullainathan has co-founded numerous startups and non-profits in the AI Healthcare space, including Nightingale, a computational medicine initiative; Pique, an app that changes how people read books and learn; and Dandelion, a company that catalyzes AI in healthcare. Mullainathan received a PhD in economics from Harvard University and a BA in computer science, economics, and mathematics from Cornell University.

Selected Publications

  • Mullainathan, S. (2025). Economics in the age of algorithms. AEA Papers and Proceedings, 115, 1–23.
  • Vafa, K., Chen, J. Y., Rambachan, A., Kleinberg, J., & Mullainathan, S. (2024). Evaluating the world model implicit in a generative model. In Advances in Neural Information Processing Systems 37: Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS) (pp. 26941–26975).
  • Kleinberg, J., & Mullainathan, S. (2024). Language generation in the limit. In Advances in Neural Information Processing Systems 37: Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS) (pp. 66058–66079).

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